Applications to Modelling and Predictive Control: Development and Validation of a Semi-physical 1D Model for Virtual Engine Strategy Optimization

在机器学习中，回归通常被认为是一种黑盒方法，通常用于从假设中识别合适的函数。它的主要目的是估计一个预测函数，使未来数据的预期误差最小，而不一定是为了全面理解由系统的物理解释所决定的输出-输入关系。因此，这样的建模应用通常是高度受限的，只允许对稀疏分散的线性模型进行训练。到目前为止，通过物理系统观测形成能够进行非线性解释的智能算法的任务很少被考虑，但它仍然构成了系统辨识领域的基础。通常，非线性系统辨识的方法包括对时间演化进行自回归建模或利用Volterra级数多维卷积积分。该项目将解决与传统的基于地图的控制器设计和动力总成开发中使用的校准实践相关的缺点。通过基于有监督神经网络的机器学习算法进行系统优化，将为系统辨识提供新颖的、未来的、非线性的物理因果关系预测建模和实验方法，从而得出具有实时能力的控制系统。该项目将与科尼赛克汽车公司AB和自由斗士AB合作开发新型无凸轮发动机技术自由斗士，以实现未来动力总成的重大效率和动力改进。它将使Frevalve的潜力得到充分利用，减少有害气体和颗粒物的排放，使该技术处于市场领先地位，为大规模实施做好准备。

In machine learning, regression is often considered a black box methodology used commonly for identification of suitable functions from a hypothesis. Its primary aim is to estimate a predicted function which leads to minimal expected error on future data, and not necessarily to benefit the overall understanding of the derived output-input relationship governed by the physical interpretation of the system. Consequently, such modelling applications are often highly constrained, allowing training solely for thinly dispersed linear models. To this day, the task of forming intelligent algorithms capable of nonlinear interpretations through physical system observation has received very little consideration, it nevertheless forms the foundation of the field of system identification. Typically, approaches for nonlinear system identification include autoregressively modelling time evolution or utilising Volterra series multidimensional convolution integrals. This project will address the shortcomings associated with the conventional map-based controller design and calibration practices used in powertrain development. It will provide novel, futuristic, non-linear physical causality predictive modelling and experimental approaches for system identification to conclude a real-time capable control system through supervised neural network-based machine learning algorithms for system optimization. The project will be undertaken in collaboration with Koenigsegg Automotive AB and Freevalve AB on the novel cam-less engine technology, Freevalve, enabling major efficiency and power improvements for future powertrains. It will enable the full utilization of Freevalve's potential and the reduction of harmful gaseous and particulate matter emissions, putting the technology in a market-leading position ready for large-scale implementation.